Lead-Machine Learning Engineer AI For Manufacturing

Key Responsibilities

• Develop and deploy AI/ML models for manufacturing resource allocation process optimization, predictive analytics, and process control.
• Build models for:
• Dynamic manufacturing infrastructure allocation across the portfolio for optimal outcomes in production of vaccines and therapeutics for public health impact and return on investment
• Develop AI/ML models to support regulatory dossier preparation, submission filings, and document automation
• Process monitoring and predictive maintenance
• Yield optimization and process robustness
• Process parameter optimization (CPP/CQA)
• Batch failure prediction and deviation detection
• Quality prediction and root-cause analysis
• Develop automated data pipelines, dashboards, and MLOps workflows for real-time analytics and decision support.
• Collaborate with manufacturing, process development, quality, automation, and IT teams to implement AI-driven solutions.
• Support data integrity, validation, and regulatory compliance for AI-enabled systems.

Qualifications

• MS/PhD in Machine Learning, Computer Science, Data Science, Chemical Engineering, Bioengineering, Industrial Engineering, or related field.
• 5-8+ years of experience in machine learning, manufacturing analytics, process engineering, or pharmaceutical/biotech manufacturing.

Technical Skills

• Strong expertise in machine learning, predictive modeling, optimization, and time-series analysis (e.g., anomaly detection, sensor/IoT data, digital twins).
• Experience with knowledge graphs, vector databases, and AI agents.
• Proficiency in Python, SQL, and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Experience with manufacturing/process data systems (MES, SCADA, LIMS, or historian platforms such as Honeywell PHD/AspenTech) is plus.
• Familiarity with process engineering or pharmaceutical manufacturing preferred.
• Understanding of public health priorities and the vaccines market preferred.

Knowledge of MLOps, cloud computing, APIs, and workflow automation, including real-time/edge inference for shop-floor deployment